Mainstreaming Controlled Traffic Techniques and OptimiSation of Movements



Controlled Traffic Farming (CTF) management can play a key role in sustaining soils and future crop production, which are today threatened by heavy machinery traffic and intensive production systems. To play this role in sustainable intensification, CTF needs to be developed to become a mainstream technology rather than a niche practice.

The overall objective of this project is to develop an integrated CTF innovation package based on research, operational tools and decision support systems which will underpin the wider adoption of CTF and related position based technologies. Thus, this project in seeking to provide a solution to CTF and related position-based machine technology adoption, using a multi-actor approach, proposes to produce quantitative research data to demonstrate the benefits for CTF adoption across a wider range of European growing conditions, using existing dedicated optimization tools on vehicle routing, resource allocation, operations scheduling, etc., to show the benefit and the importance of adapting the technology use to the constraints of the implemented CTF system, through the evaluation of specific scenarios involving, for example, the effect of the design and configuration of the CTF, the optimal routing pattern, etc.

Transnational implementation will increase adoption of proposed technology by moving it from being a ‘niche’ technology to being a ‘mainstream’ practice. This will involve: applied research which will quantify and illustrate the benefits of controlled traffic and related technologies in terms of crop, soil, and machine efficiency and; development of innovative DSS and operational tools which will allow CTF technologies to be optimized. Optimization of agricultural logistics will be based on the Geo-spatial Arable field optimization Service (GAOS) and implemented in a Service Oriented Architecture (SoA).

Designated field trials will be carried out in four countries: Belgium, Ireland, the Netherlands and Denmark. Using information on field size and machinery size, simple modelling will be used to scale up the research results to individual farm and regional levels.